Introduction to R: Linear Regression

This lesson covers the basics of linear regression in R. It includes a discussion of basic linear regression, polynomial regression and multiple linear regression as well as some assumptions and potential sources of problems when making linear regression models.

This is lesson 27 of a 30-part introduction to the R programming language for data analysis and predictive modeling. Link to the code notebook below:

Intro to R: Linear Regression

This guide does not assume any prior exposure to R, programming or data science. It is intended for beginners with an interest in data science and those who might know other programming languages and would like to learn R.

I will create the videos for this guide such that you should be able to learn a lot just watching on YouTube, but to get the most out of the guide, it is recommended that you create a Kaggle account so that you can fork and edit each lesson so that you can follow along and run code yourself.

Introduction to R Playlist:

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In Kaggle, the RMSE for both models (23:18) is actually the same, and this is true whether using the manually created function from earlier or the built-in RMSE function in caret. Since the models are different and the plot shows a better fit for the quadratic model, shouldn't these RMSE values differ? Also, thanks for these great videos! They're helping me learn to do stats for my research and are so well-written and clearly explained. For a total beginner like me they are an invaluable resource.